1 Load data

dir <- getwd()
date <- params$data_date

price_list_buys <- read.csv(paste(dir, "/../data/raw/gw2-all-buys-raw-", date, ".csv", sep = ""))
price_list_sells <- read.csv(paste(dir, "/../data/raw/gw2-all-sells-raw-", date, ".csv", sep = ""))
item_list <- read.csv(paste(dir, "/../data/raw/gw2-all-items-raw-", date, ".csv", sep = ""))

df_buys <- item_list %>% left_join(price_list_buys)
df_sells <- item_list %>% left_join(price_list_sells)

rm(dir)
rm(date)
rm(item_list)
rm(price_list_sells)
rm(price_list_buys)

1.1 Data cleaning

df_sells <- df_sells %>% 
  drop_na(unit_price, quantity) %>% 
  mutate(rarity = as.factor(rarity),
         type = as.factor(type),
         item_type = as.factor(item_type),
         item_weight_class = as.factor(item_weight_class),
         unit_price_gold = unit_price / 10000) %>% 
  select(-unit_price)

df_buys <- df_buys %>% 
  drop_na(unit_price, quantity) %>% 
  mutate(rarity = as.factor(rarity),
         type = as.factor(type),
         item_type = as.factor(item_type),
         item_weight_class = as.factor(item_weight_class),
         unit_price_gold = unit_price / 10000) %>% 
  select(-unit_price)

2 Data Exploration

2.1 Types

df_types <- df_sells %>% 
  distinct(type, name) %>%
  group_by(type) %>% 
  count(type)

df_types %>% 
  ggplot() +
  geom_bar(aes(x = type, y = n, fill = type), stat='identity') +
  theme(axis.text.x = element_text(angle = 45, hjust=1), legend.position="bottom") +
  labs(title = "Type distribution", subtitle = "of all types", fill = "Types",
    x = "Type", y = "Count", caption = paste("Data from", params$data_date))

There are a lot of different types of items. As we can see, Armor, Consumables and Weapons are the most common items.

This means there are very many different weapon, consumable and armor items in the game, but not that many different crafting materials and upgrade components which seems reasonable.

Armors and Weapons can be further broken down into sub types:

df_item_types <- df_sells %>% 
  filter(type %in% c("Armor", "Weapon")) %>% 
  distinct(type, item_type, item_weight_class, name) %>%
  group_by(type, item_type, item_weight_class) %>% 
  count(item_type)

df_item_types %>% 
  ggplot() +
  geom_bar(aes(x = item_type, y = n, fill = item_weight_class), stat='identity') +
  facet_grid(df_item_types$type) +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.3, hjust=1)) +
  labs(title = "Detailed item types", subtitle = "Split by armors and weapons", fill = "Item weight",
    x = "Item types", y = "Count", caption = paste("Data from", params$data_date))

rm(df_item_types)

For armors we have the distinction between Light (for mages), Medium (for rangers) and Heavy (for warriors). Clothing are special items for the style points. There is no such distinction for weapons. Different classes can use different weapons, but all of them can be used by more than one class.

2.2 Rarity of items

There are different rarities in Guild Wars that suggest how often this item will occur in the game:

rarities <- c("Junk", "Basic", "Fine", "Masterwork", "Rare", "Exotic", "Ascended", "Legendary")
colors <- 
  c("Junk" = "#a3aeb9",
    "Basic" = "#000000",
    "Fine" = "#6a9cd9", 
    "Masterwork" = "#17911a", 
    "Rare" = "#f0cd25",
    "Exotic" = "#e9ad1a",
    "Ascended" = "#e64680",
    "Legendary" = "#53238a")

data.frame(rarities) %>% 
  ggplot() +
  geom_bar(aes(x = rarities, fill = rarities)) +
  scale_x_discrete(limits = rarities) +
  scale_fill_manual("Rarity", values = colors) + 
  theme(axis.text.y=element_blank(),
        axis.title.y=element_blank(),
        axis.title.x=element_blank())

What is obvious by the names, rarity on the left is in general more common than rarity on the right.

One important information is that a lot of very rare items (Ascended and Legendary) can’t be sold to other players. That’s the reason we see much more Rare and Exotic items being sold for high prices than those really rare ones. Junk is zero because it can only be sold at non-player vendors.

In our dataset, we have the following distribution:

df <- df_sells %>% 
  count(rarity) %>% 
  arrange(desc(n))

df %>% 
  ggplot() +
  geom_bar(aes(x = rarity, y = n, fill = rarity), stat='identity') +
  scale_x_discrete(limits = rarities) +
  scale_fill_manual("Rarity", values = colors) +
  labs(title = "Rarity distribution", subtitle = "of all raritires", fill = "Rarity",
    x = "Rarity", y = "Count", caption = paste("Data from", params$data_date))

relevant_rarities <- df %>% 
  filter(n > 100000)

relevant_rarities <- relevant_rarities$rarity

2.3 Price distribution

Now let’s not only look at the top five items, but at the distribution in general.

df_sells_distribution <- df_sells %>% 
  group_by(name) %>% 
  summarise(mean_unit_price_gold = mean(unit_price_gold),
            type = unique(type),
            rarity = unique(rarity)) %>% 
  arrange(desc(mean_unit_price_gold))
## `summarise()` has grouped output by 'name'. You can override using the
## `.groups` argument.
df_sells_distribution %>% 
  ggplot() +
  geom_histogram(aes(x =  mean_unit_price_gold, fill = type), stat="count") +
  scale_x_binned(limits = c(0, 1000)) +
  labs(x = "Mean price", y = "Count",
       title = "Item price distribution", subtitle = "Items being sold (offers), in gold", 
       caption = paste("Data from", params$data_date))
## Warning: Ignoring unknown parameters: binwidth, bins, pad

As we can see, prices are common between 0 - 100 gold. What we also see is that there are a lot of different types of items. We can strip this down so we only see the relevant types.

relevant_types <- df_sells_distribution %>% 
  group_by(type) %>% 
  count() %>% 
  filter(n > 350)

df_sells_distribution <- df_sells_distribution %>% 
  filter(type %in% C(relevant_types$type))

df_sells_distribution %>% 
  ggplot() +
  geom_histogram(aes(x =  mean_unit_price_gold, fill = type), stat="count") +
  scale_x_binned(limits = c(0, 50), breaks = c(0, 10, 25, 50)) +
  facet_grid(. ~ type) + 
  labs(x = "Mean price", y = "Count",
       title = "Item price distribution", subtitle = "Items being sold (offers), in gold", 
       caption = paste("Data from", params$data_date))
## Warning: Ignoring unknown parameters: binwidth, bins, pad

2.4 Crafting Material

Let’s have a closer look on one of the most important resources in the game: Crafting material.

The base material can be collected everywhere in the world from trees, stones or herbs. As a player, you can learn up to two professions that can improve those base materials to something better and useful.

Let’s first check if there are any outliers in the data that we should clean.

df_crafting_sells <- df_sells %>% 
  filter(type == "CraftingMaterial")

df_crafting_buys <- df_buys %>% 
  filter(type == "CraftingMaterial")

ggplot() +  
  geom_boxplot(data = df_crafting_sells, aes(x = 'Sells', y = unit_price_gold)) +
  geom_boxplot(data = df_crafting_buys, aes(x = 'Buys', y = unit_price_gold)) +
  geom_hline(yintercept = 999, linetype="dashed", color = "red") +
  labs(title = "Outliers on crafting material", subtitle = "Baseline at 999 gold",
    x = "", y = "Price in gold", caption = paste("Data from", params$data_date))

We can see that on the buys, there aren’t any outliers because the human brain is still working and no one buys overpriced goods. On the other hand, on the sells there are some people who are exaggerating with the prices. Let’s strip everything at 1000 and above.

df_sells_crafting_material <-  df_sells %>% 
  filter(type == "CraftingMaterial",
         unit_price_gold < 1000) %>% 
  group_by(rarity, name) %>% 
   summarize(
    mean_price = mean(unit_price_gold),
    sum_quantity = sum(quantity),
    picture = web_image(unique(icon), height = 25)
  ) %>% 
  mutate(
    quantity_label = label_number_si(accuracy=0.1)(sum_quantity),
    mean_price_gold = round(mean_price, digits = 3)) %>% 
  arrange(desc(sum_quantity)) %>% 
  select(rarity, name, mean_price_gold, quantity_label, sum_quantity, picture)

df_sells_crafting_material %>% 
  datatable(extensions = c('ColReorder', 'Buttons', 'Responsive', 'Scroller', 'SearchPanes', 'Select'),
            options = list(colReorder = TRUE,
                           dom = 'Bfrtip', 
                           buttons = list('searchPanes', 'copy', list(
                                           extend = 'collection',
                                           buttons = c('csv', 'excel', 'pdf'), 
                                           text = 'Download'))),
            escape = FALSE,
            colnames=c("#", "Rarity", "Name", "Mean price in gold", "Quantity (short)", "Quantity", "Icon")
            )
exp_sell_item_name <- df_sells_crafting_material %>% 
  arrange(desc(mean_price_gold)) %>% 
  ungroup() %>%
  select(name) %>% 
  first() %>% 
  first()

2.5 Understanding offers and orders

On the auction house, one can create an offer (to sell items) or an order (to buy items).

If an offer is created, the game automatically searches for the highest order price and if that’s fine for the seller, the item will be sold and sent. But the seller can also create an offer for a specific price (higher than the other orders) and wait, until someone wants to buy the item for the wanted amount.

The same is true for orders. Items can be bought immediately from the lowest offer or an order is created (lower than the other offers) and will wait, until someone sells the item for the wanted amount.

Let’s make an example:

df_top5 <- df_sells %>% 
  group_by(name) %>% 
  summarise(mean_unit_price_gold = mean(unit_price_gold), 
            mean_vendor_value_gold = mean(vendor_value) / 10000,
            type = unique(type),
            rarity = unique(rarity)) %>% 
  arrange(desc(mean_unit_price_gold)) %>% 
  ungroup() %>% 
  top_n(5, mean_unit_price_gold)

df_top5 %>% 
  ggplot() +
  geom_bar(aes(x = name, y = mean_unit_price_gold, fill = rarity), stat="identity") +
  geom_point(aes(x = name, y = mean_vendor_value_gold), fill = "#000000", stat="identity") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1)) +
  scale_x_discrete(limits = df_top5$name) +
  labs(x = "Name", y = "Mean price",
       title = "Five most expensive items", subtitle = "Items being sold (offers), in gold", 
       caption = paste("Data from", params$data_date))

highest_offer <- df_top5 %>% 
  select(mean_unit_price_gold) %>%
  first() %>% 
  first()

rm(df_top5)

Our top item here, the Festive Grymm Svaard, is a cosmetic item that can only be bought with real money and a lot of luck. It costs 10^{4} gold which seems to be the maximum possible price for an auction. This is only an offer, someone wants to sell this item for this price, so we’ll have to find out if anyone would ever buy this.

df_buys_top5_grymm <- df_buys %>% 
  filter(name == 'Festive Grymm Svaard') %>% 
  select(name, unit_price_gold) %>%
  top_n(5) %>% 
  arrange(desc((unit_price_gold)))

df_buys_top5_grymm %>% 
  gt(rowname_col = "name") %>%
  tab_header(title = "Orders of the Festive Grymm Svaard") %>%
  fmt_number(
    columns = unit_price_gold
  ) %>% 
  cols_label(
    unit_price_gold = "Unit price (in gold)"
  )
Orders of the Festive Grymm Svaard
Unit price (in gold)
Festive Grymm Svaard 300.70
Festive Grymm Svaard 300.70
Festive Grymm Svaard 276.47
Festive Grymm Svaard 76.47
Festive Grymm Svaard 76.47
 
highest_order <- df_buys_top5_grymm %>%
  select(unit_price_gold) %>% 
  first() %>% 
  first()

rm(df_buys_top5_grymm)

As we can see, the highest order on this item is 300.6983 gold. That’s pretty much, but far away from 10^{4}.

If someone is able to buy this item for 300.6983 gold and sell it again for 10^{4} that would be a good fortune.

Because the prices are so different, it’s unlikely that this will happen.

But who doesn’t want to look that fancy.

The Festive Grymm Svaard skin.